Refactor models/export.py arguments (#3564)

* Refactor models/export.py arguments

* cleanup

* cleanup

models/export.py

@@ -1,4 +1,4 @@
-"""Exports a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
+"""Export a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
 
 Usage:
     $ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1
@@ -21,42 +21,39 @@ from utils.activations import Hardswish, SiLU
 from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging
 from utils.torch_utils import select_device
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats')
-    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
-    parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
-    parser.add_argument('--train', action='store_true', help='model.train() mode')
-    parser.add_argument('--optimize', action='store_true', help='optimize TorchScript for mobile')  # TorchScript-only
-    parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes')  # ONNX-only
-    parser.add_argument('--simplify', action='store_true', help='simplify ONNX model')  # ONNX-only
-    parser.add_argument('--opset-version', type=int, default=12, help='ONNX opset version')  # ONNX-only
-    opt = parser.parse_args()
-    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
-    opt.include = [x.lower() for x in opt.include]
-    print(opt)
-    set_logging()
+
+def export(weights='./yolov5s.pt',  # weights path
+           img_size=(640, 640),  # image (height, width)
+           batch_size=1,  # batch size
+           device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+           include=('torchscript', 'onnx', 'coreml'),  # include formats
+           half=False,  # FP16 half-precision export
+           inplace=False,  # set YOLOv5 Detect() inplace=True
+           train=False,  # model.train() mode
+           optimize=False,  # TorchScript: optimize for mobile
+           dynamic=False,  # ONNX: dynamic axes
+           simplify=False,  # ONNX: simplify model
+           opset_version=12,  # ONNX: opset version
+           ):
     t = time.time()
+    include = [x.lower() for x in include]
+    img_size *= 2 if len(img_size) == 1 else 1  # expand
 
     # Load PyTorch model
-    device = select_device(opt.device)
-    assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
-    model = attempt_load(opt.weights, map_location=device)  # load FP32 model
+    device = select_device(device)
+    assert not (device.type == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
+    model = attempt_load(weights, map_location=device)  # load FP32 model
     labels = model.names
 
     # Input
     gs = int(max(model.stride))  # grid size (max stride)
-    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
-    img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device)  # image size(1,3,320,192) iDetection
+    img_size = [check_img_size(x, gs) for x in img_size]  # verify img_size are gs-multiples
+    img = torch.zeros(batch_size, 3, *img_size).to(device)  # image size(1,3,320,192) iDetection
 
     # Update model
-    if opt.half:
+    if half:
         img, model = img.half(), model.half()  # to FP16
-    model.train() if opt.train else model.eval()  # training mode = no Detect() layer grid construction
+    model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
     for k, m in model.named_modules():
         m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
         if isinstance(m, models.common.Conv):  # assign export-friendly activations
@@ -65,42 +62,42 @@ if __name__ == '__main__':
             elif isinstance(m.act, nn.SiLU):
                 m.act = SiLU()
         elif isinstance(m, models.yolo.Detect):
-            m.inplace = opt.inplace
-            m.onnx_dynamic = opt.dynamic
+            m.inplace = inplace
+            m.onnx_dynamic = dynamic
             # m.forward = m.forward_export  # assign forward (optional)
 
     for _ in range(2):
         y = model(img)  # dry runs
-    print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)")
+    print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
 
     # TorchScript export -----------------------------------------------------------------------------------------------
-    if 'torchscript' in opt.include or 'coreml' in opt.include:
+    if 'torchscript' in include or 'coreml' in include:
         prefix = colorstr('TorchScript:')
         try:
             print(f'\n{prefix} starting export with torch {torch.__version__}...')
-            f = opt.weights.replace('.pt', '.torchscript.pt')  # filename
+            f = weights.replace('.pt', '.torchscript.pt')  # filename
             ts = torch.jit.trace(model, img, strict=False)
-            (optimize_for_mobile(ts) if opt.optimize else ts).save(f)
+            (optimize_for_mobile(ts) if optimize else ts).save(f)
             print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
         except Exception as e:
             print(f'{prefix} export failure: {e}')
 
     # ONNX export ------------------------------------------------------------------------------------------------------
-    if 'onnx' in opt.include:
+    if 'onnx' in include:
         prefix = colorstr('ONNX:')
         try:
             import onnx
 
             print(f'{prefix} starting export with onnx {onnx.__version__}...')
-            f = opt.weights.replace('.pt', '.onnx')  # filename
-            torch.onnx.export(model, img, f, verbose=False, opset_version=opt.opset_version,
-                              training=torch.onnx.TrainingMode.TRAINING if opt.train else torch.onnx.TrainingMode.EVAL,
-                              do_constant_folding=not opt.train,
+            f = weights.replace('.pt', '.onnx')  # filename
+            torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version,
+                              training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
+                              do_constant_folding=not train,
                               input_names=['images'],
                               output_names=['output'],
                               dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
                                             'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
-                                            } if opt.dynamic else None)
+                                            } if dynamic else None)
 
             # Checks
             model_onnx = onnx.load(f)  # load onnx model
@@ -108,7 +105,7 @@ if __name__ == '__main__':
             # print(onnx.helper.printable_graph(model_onnx.graph))  # print
 
             # Simplify
-            if opt.simplify:
+            if simplify:
                 try:
                     check_requirements(['onnx-simplifier'])
                     import onnxsim
@@ -116,8 +113,8 @@ if __name__ == '__main__':
                     print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
                     model_onnx, check = onnxsim.simplify(
                         model_onnx,
-                        dynamic_input_shape=opt.dynamic,
-                        input_shapes={'images': list(img.shape)} if opt.dynamic else None)
+                        dynamic_input_shape=dynamic,
+                        input_shapes={'images': list(img.shape)} if dynamic else None)
                     assert check, 'assert check failed'
                     onnx.save(model_onnx, f)
                 except Exception as e:
@@ -127,15 +124,15 @@ if __name__ == '__main__':
             print(f'{prefix} export failure: {e}')
 
     # CoreML export ----------------------------------------------------------------------------------------------------
-    if 'coreml' in opt.include:
+    if 'coreml' in include:
         prefix = colorstr('CoreML:')
         try:
             import coremltools as ct
 
             print(f'{prefix} starting export with coremltools {ct.__version__}...')
-            assert opt.train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
+            assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
             model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
-            f = opt.weights.replace('.pt', '.mlmodel')  # filename
+            f = weights.replace('.pt', '.mlmodel')  # filename
             model.save(f)
             print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
         except Exception as e:
@@ -143,3 +140,24 @@ if __name__ == '__main__':
 
     # Finish
     print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
+    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image (height, width)')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats')
+    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
+    parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
+    parser.add_argument('--train', action='store_true', help='model.train() mode')
+    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
+    parser.add_argument('--dynamic', action='store_true', help='ONNX: dynamic axes')
+    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
+    parser.add_argument('--opset-version', type=int, default=12, help='ONNX: opset version')
+    opt = parser.parse_args()
+    print(opt)
+    set_logging()
+
+    export(**vars(opt))